Options & libs
# Turning scientific / Exponential numbers off
options(scipen = 999)
library(tidyverse)
library(tidytuesdayR)
library(ggthemes)
library(glue)
library(scales)
view missing data
Creating & setting custom theme
theme_viny_bright <- function(){
library(ggthemes)
ggthemes::theme_fivethirtyeight() %+replace%
theme(
axis.title = element_text(size = 9),
axis.text = element_text(size = 8),
legend.text = element_text(size = 7),
panel.background = element_rect(fill = "white"),
plot.background = element_rect(fill = "white"),
strip.background = element_blank(),
legend.background = element_rect(fill = NA),
legend.key = element_rect(fill = NA),
plot.title = element_text(hjust = 0.5,
size = 16,
face = "bold"),
plot.subtitle = element_text(hjust = 0.5, size = 10, face = "bold"),
plot.caption = element_text(hjust = 1, size = 8)
)
}
theme_set(theme_viny_bright())
sources:
Inspired from: https://www.youtube.com/watch?v=EHqFDXa-sH4&t=2105s
Loading data
tt <- tt_load("2021-03-02")
tt
EDA
youtube <- tt$youtube
youtube
youtube %>%
mutate_if(is.character, as.factor) %>%
summary()
missing value
naniar::gg_miss_upset(data = youtube)


youtube %>%
count(brand) %>%
mutate(brand = fct_reorder(brand, n)) %>%
ggplot(aes(x = n, y = brand)) +
geom_col()

youtube %>%
ggplot(aes(x = year, fill = brand)) +
geom_bar() +
facet_wrap(~brand) +
guides(x = guide_axis(n.dodge = 2))

youtube %>%
na.omit() %>%
mutate(brand = fct_reorder(brand, view_count)) %>%
ggplot(aes(x = view_count, y = brand)) +
geom_boxplot() +
# geom_jitter(alpha = 0.3) +
scale_x_log10(labels = comma)

youtube %>%
na.omit() %>%
mutate(brand = fct_reorder(brand, view_count)) %>%
ggplot(aes(x = view_count, y = brand)) +
geom_violin(draw_quantiles = c(0.25, 0.5, 0.75)) +
geom_jitter(alpha = 0.3) +
scale_x_log10(labels = comma)

youtube %>%
na.omit() %>%
mutate(brand = fct_reorder(brand, view_count)) %>%
ggplot(aes(x = view_count, y = brand, fill = funny)) +
geom_boxplot() +
# geom_jitter(alpha = 0.3) +
scale_x_log10(labels = comma)

youtube %>%
na.omit() %>%
mutate(brand = fct_reorder(brand, view_count)) %>%
ggplot(aes(x = view_count, y = brand, fill = use_sex)) +
geom_boxplot() +
# geom_jitter(alpha = 0.3) +
scale_x_log10(labels = comma)

youtube %>%
pivot_longer(names_to = "ads_category", values_to = "true_false",
cols = funny:use_sex)
popular categories in brands
youtube %>%
pivot_longer(names_to = "ads_category", values_to = "true_false",
cols = funny:use_sex) %>%
filter(true_false == "TRUE") %>%
ggplot(aes(y = ads_category, x = view_count)) +
geom_col() +
facet_wrap(~brand)

log of view_count
youtube %>%
pivot_longer(names_to = "ads_category", values_to = "true_false",
cols = funny:use_sex) %>%
filter(true_false == "TRUE") %>%
ggplot(aes(y = fct_reorder(ads_category, view_count),
x = view_count)) +
geom_col() +
facet_wrap(~brand) +
scale_x_log10()

reorder_within by view_count
youtube %>%
na.omit() %>%
pivot_longer(names_to = "ads_category", values_to = "true_false",
cols = funny:use_sex) %>%
filter(true_false == "TRUE",
view_count > 10000) %>%
group_by(brand, ads_category, view_count) %>%
summarise(view_count = sum(view_count)) %>%
ungroup() %>%
mutate_if(is.character, factor) %>%
mutate(ads_category = reorder_within(x = ads_category, by = view_count, within = brand)) %>%
ggplot(aes(y = ads_category, x = view_count)) +
geom_col() +
facet_wrap(~brand, scales = "free_y") +
scale_y_reordered() +
theme(axis.text.x = element_text(angle = 90)) +
scale_x_continuous(labels = unit_format(unit = "M", scale = 1e-6))

gathered_categories <- youtube %>%
na.omit() %>%
gather(ads_category, true_false, funny:use_sex)
gathered_categories
view_count by pct
gathered_categories %>%
filter(true_false == "TRUE",
) %>% # view_count > 10000
group_by(brand, ads_category) %>%
summarise(view_count = sum(view_count)) %>%
group_by(brand) %>%
mutate(pct = view_count / sum(view_count)) %>%
# mutate_if(is.character, factor) %>%
mutate(ads_category = reorder_within(x = ads_category, by = pct, within = brand)) %>%
ggplot(aes(y = ads_category, x = view_count)) +
geom_col() +
facet_wrap(~brand, scales = "free_y") +
scale_y_reordered() +
theme(axis.text.x = element_text(angle = 90)) +
scale_x_continuous(labels = unit_format(unit = "M", scale = 1e-6))

by pct
wrapping axis text using str_wrap() from: https://stackoverflow.com/questions/21878974/wrap-long-axis-labels-via-labeller-label-wrap-in-ggplot2
gathered_categories %>%
filter(true_false == "TRUE",
) %>% # view_count > 10000
group_by(brand, ads_category) %>%
summarise(view_count = sum(view_count)) %>%
group_by(brand) %>%
mutate(pct = view_count / sum(view_count),
ads_category = str_replace_all(ads_category,"_"," "),
ads_category = str_wrap(ads_category, width = 10)) %>%
# mutate_if(is.character, factor) %>%
mutate(ads_category = reorder_within(x = ads_category, by = pct, within = brand)
) %>%
ggplot(aes(y = ads_category, x = pct)) +
geom_col() +
facet_wrap(~brand, scales = "free_y") +
scale_y_reordered() +
theme(axis.text.x = element_text(angle = 90)) +
scale_x_continuous(labels = percent)

pct+view_count labels
Adding view_count to axis text in above chart
gathered_categories %>%
filter(true_false == "TRUE",
) %>% # view_count > 10000
group_by(brand, ads_category) %>%
summarise(view_count = sum(view_count)) %>%
group_by(brand) %>%
mutate(pct = view_count / sum(view_count),
ads_category = str_replace_all(ads_category,"_"," "),
ads_category = paste(ads_category,view_count),
ads_category = str_wrap(ads_category, width = 10)) %>%
# mutate_if(is.character, factor) %>%
mutate(ads_category = reorder_within(x = ads_category, by = pct, within = brand)
) %>%
ggplot(aes(y = ads_category, x = pct)) +
geom_col() +
facet_wrap(~brand, scales = "free_y") +
scale_y_reordered() +
theme(axis.text.x = element_text(angle = 90)) +
scale_x_continuous(labels = percent)

test_set = data.frame(x = c("label", "long label", "very,_very_long_label"),
y = c(10, 15, 20))
test_set$newx = str_wrap(test_set$x, width = 10)
ggplot(test_set, aes(newx, y)) +
xlab("") + ylab("Number of Participants") +
geom_bar(stat = "identity")

youtube %>%
na.omit() %>%
gather(key = category, value = value, funny:use_sex) %>%
group_by(category,
year = 2 * (year %/% 2)) %>%
summarise(pct = mean(value), n = n()) %>%
ggplot(aes(x = year, y = pct, col = category)) +
geom_path(size = .9) +
scale_y_continuous(labels = percent) +
facet_wrap(~category) +
theme(legend.position = "none")

youtube %>%
na.omit() %>%
gather(key = category, value = value, funny:use_sex) %>%
group_by(category = str_to_title(str_replace_all(category, "_", " ")),
year = 2 * (year %/% 2)) %>%
summarise(pct = mean(value),
n = n()) %>%
ggplot(aes(x = year, y = pct, col = category)) +
geom_path(size = .9) +
scale_y_continuous(labels = percent) +
facet_wrap(~category) +
theme(legend.position = "none")

Testing Statistically
checking trend statistically related to year or not
glm(animals ~ year, family = "binomial", data = youtube) %>%
summary()
Statistically above relation doesn’t exist
glm(celebrity ~ year, family = "binomial", data = youtube) %>%
summary()
statistically year has relationship with celebrity
broom loops
Running loop on all using broom
youtube %>%
gather(category, value, funny:use_sex) %>%
group_by(category) %>%
nest()
youtube %>%
gather(category, value, funny:use_sex) %>%
group_by(category) %>%
summarise(model = list(glm(value ~ year, family = "binomial") )) %>%
mutate(td_modeldata = map(model, broom::tidy))
coefficients <- youtube %>%
# na.omit() %>%
gather(category, value, funny:use_sex) %>%
group_by(category) %>%
summarise(model = list(glm(value ~ year, family = "binomial") )) %>%
mutate(td_modeldata = map(model, broom::tidy)) %>%
unnest(td_modeldata) %>%
filter(term != "(Intercept)") %>%
arrange(desc(estimate))
coefficients
Categories with Only significant relationship with year
youtube %>%
na.omit() %>%
gather(key = category, value = value, funny:use_sex) %>%
group_by(category,
year = 2 * (year %/% 2)) %>%
summarise(pct = mean(value),
n = n()) %>%
inner_join(coefficients, by = "category") %>%
mutate(category = str_to_title(str_replace_all(category, "_", " "))) %>%
filter(p.value <= .01) %>%
ggplot(aes(x = year, y = pct, col = category)) +
geom_path(size = .9) +
scale_y_continuous(labels = percent) +
facet_wrap(~category) +
theme(legend.position = "none")

---
title: "Tidy tuesday - Unemployment EDA"
output: 
  html_notebook:
    highlight: tango
    df_print: paged
    toc: yes
    toc_float:
      collapsed: yes
      smooth_scroll: yes
    number_sections: yes
    toc_depth: 6
  html_document:
    toc: yes
    toc_depth: '6'
    df_print: paged
---

# Options & libs

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE, results='hide', fig.keep='all', dpi = 300, fig.height = 6, fig.width = 6, out.width = "100%",attr.output='style="max-height: 300px;"')
```



```{css, echo=FALSE}
# CSS for scrollable output & Header colors

.scroll-100 {
  max-height: 100px;
  overflow-y: auto;
  background-color: inherit;
}

```


```{r}
# Turning scientific / Exponential numbers off

options(scipen = 999)
```

```{r}
library(tidyverse)
library(tidytuesdayR)
library(ggthemes)
library(glue)
library(scales)
```

view missing data

```{r}
library(naniar)
```


Creating & setting custom theme

```{r}

theme_viny_bright <- function(){
  
  library(ggthemes)
  
  ggthemes::theme_fivethirtyeight() %+replace%
  
  theme(
    axis.title = element_text(size = 9),
    axis.text = element_text(size = 8),
    legend.text = element_text(size = 7),
    panel.background = element_rect(fill = "white"),
    plot.background = element_rect(fill = "white"),
    strip.background = element_blank(),
    legend.background = element_rect(fill = NA),
    legend.key = element_rect(fill = NA),
    plot.title = element_text(hjust = 0.5,
                              size = 16,
                              face = "bold"),
    plot.subtitle = element_text(hjust = 0.5, size = 10, face = "bold"),
    plot.caption = element_text(hjust = 1, size = 8)
      )
  
  }

theme_set(theme_viny_bright())
```

**sources:**

Inspired from: https://www.youtube.com/watch?v=EHqFDXa-sH4&t=2105s

# Loading data

```{r}
tt <- tt_load("2021-03-02")
tt
```

# EDA

```{r}
youtube <- tt$youtube
youtube
```

```{r}
str(youtube)
```

```{r}
summary(youtube)
```

```{r}
youtube %>% 
  mutate_if(is.character, as.factor) %>% 
  summary()
```

missing value

```{r}
naniar::gg_miss_upset(data = youtube)
```


```{r}
youtube %>% 
  count(brand) %>% 
  
  mutate(brand = fct_reorder(brand, n)) %>% 
  
  ggplot(aes(x = n, y = brand)) +
  geom_col()
```


```{r}
youtube %>% 
  
  ggplot(aes(x = year, fill = brand)) +
  geom_bar() +
  facet_wrap(~brand) +
  guides(x = guide_axis(n.dodge = 2))
```

```{r}
youtube %>% 
  na.omit() %>% 
  mutate(brand = fct_reorder(brand, view_count)) %>% 
  
  ggplot(aes(x = view_count, y = brand)) +
  geom_boxplot() +
  # geom_jitter(alpha = 0.3) +
  scale_x_log10(labels = comma)
```


```{r}
youtube %>% 
  na.omit() %>%
  mutate(brand = fct_reorder(brand, view_count)) %>% 
   
  ggplot(aes(x = view_count, y = brand)) +
  geom_violin(draw_quantiles = c(0.25, 0.5, 0.75)) +
  geom_jitter(alpha = 0.3) +
  scale_x_log10(labels = comma)
```

```{r}
youtube %>% 
  na.omit() %>% 
  mutate(brand = fct_reorder(brand, view_count)) %>% 
  
  ggplot(aes(x = view_count, y = brand, fill = funny)) +
  geom_boxplot() +
  # geom_jitter(alpha = 0.3) +
  scale_x_log10(labels = comma)
```


```{r}
youtube %>% 
  na.omit() %>% 
  mutate(brand = fct_reorder(brand, view_count)) %>% 
  
  ggplot(aes(x = view_count, y = brand, fill = use_sex)) +
  geom_boxplot() +
  # geom_jitter(alpha = 0.3) +
  scale_x_log10(labels = comma)
```



```{r}
youtube %>% colnames()
```


```{r}
youtube %>% 
  pivot_longer(names_to = "ads_category", values_to = "true_false", 
               cols = funny:use_sex)
```

### popular categories in brands

```{r}
youtube %>% 
  pivot_longer(names_to = "ads_category", values_to = "true_false", 
               cols = funny:use_sex) %>% 
  filter(true_false == "TRUE") %>% 
  
  ggplot(aes(y = ads_category, x = view_count)) +
  geom_col() +
  facet_wrap(~brand)
```

```{r}
library(tidytext)
```

#### log of view_count

```{r}
youtube %>% 
  pivot_longer(names_to = "ads_category", values_to = "true_false", 
               cols = funny:use_sex) %>% 
  filter(true_false == "TRUE") %>% 
  
  ggplot(aes(y = fct_reorder(ads_category, view_count), 
             x = view_count)) +
  geom_col() +
  facet_wrap(~brand) +
  scale_x_log10()
```

#### reorder_within by view_count

```{r fig.width=8, fig.height=10}
youtube %>%
  na.omit() %>% 
  pivot_longer(names_to = "ads_category", values_to = "true_false", 
               cols = funny:use_sex) %>% 
  filter(true_false == "TRUE",
         view_count > 10000) %>% 
  group_by(brand, ads_category, view_count) %>% 
  summarise(view_count = sum(view_count)) %>% 
  ungroup() %>% 
  mutate_if(is.character, factor) %>% 
  mutate(ads_category = reorder_within(x = ads_category, by = view_count, within = brand)) %>% 
  
  ggplot(aes(y = ads_category, x = view_count)) +
  geom_col() +
  facet_wrap(~brand, scales = "free_y") +
  scale_y_reordered() +
  theme(axis.text.x = element_text(angle = 90)) +
  scale_x_continuous(labels = unit_format(unit = "M", scale = 1e-6))
```

```{r}
gathered_categories <- youtube %>% 
  na.omit() %>% 
  gather(ads_category, true_false, funny:use_sex)

gathered_categories
```

#### view_count by pct

```{r fig.width=8, fig.height=10}
gathered_categories %>% 
  filter(true_false == "TRUE",
         ) %>% # view_count > 10000
  
  group_by(brand, ads_category) %>% 
  summarise(view_count = sum(view_count)) %>% 
  
  group_by(brand) %>% 
  mutate(pct = view_count / sum(view_count)) %>% 
  
  # mutate_if(is.character, factor) %>% 
  mutate(ads_category = reorder_within(x = ads_category, by = pct, within = brand)) %>% 
  
  ggplot(aes(y = ads_category, x = view_count)) +
  geom_col() +
  facet_wrap(~brand, scales = "free_y") +
  scale_y_reordered() +
  
  theme(axis.text.x = element_text(angle = 90)) +
  scale_x_continuous(labels = unit_format(unit = "M", scale = 1e-6))
```


#### by pct

wrapping axis text using str_wrap() from: https://stackoverflow.com/questions/21878974/wrap-long-axis-labels-via-labeller-label-wrap-in-ggplot2

```{r fig.width=8, fig.height=10}
gathered_categories %>% 
  filter(true_false == "TRUE",
         ) %>% # view_count > 10000
  
  group_by(brand, ads_category) %>% 
  summarise(view_count = sum(view_count)) %>% 
  
  group_by(brand) %>% 
  mutate(pct = view_count / sum(view_count),
         ads_category = str_replace_all(ads_category,"_"," "),
         ads_category = str_wrap(ads_category, width = 10)) %>% 
  
  # mutate_if(is.character, factor) %>% 
  mutate(ads_category = reorder_within(x = ads_category, by = pct, within = brand)
         ) %>%
  
  ggplot(aes(y = ads_category, x = pct)) +
  geom_col() +
  facet_wrap(~brand, scales = "free_y") +
  scale_y_reordered() +
  
  theme(axis.text.x = element_text(angle = 90)) +
  scale_x_continuous(labels = percent)
```

#### pct+view_count labels

Adding view_count to axis text in above chart

```{r fig.width=8, fig.height=10}
gathered_categories %>% 
  filter(true_false == "TRUE",
         ) %>% # view_count > 10000
  
  group_by(brand, ads_category) %>% 
  summarise(view_count = sum(view_count)) %>% 
  
  group_by(brand) %>% 
  mutate(pct = view_count / sum(view_count),
         ads_category = str_replace_all(ads_category,"_"," "),
         ads_category = paste(ads_category,view_count),
         ads_category = str_wrap(ads_category, width = 10)) %>% 
  
  # mutate_if(is.character, factor) %>% 
  mutate(ads_category = reorder_within(x = ads_category, by = pct, within = brand)
         ) %>%
  
  ggplot(aes(y = ads_category, x = pct)) +
  geom_col() +
  facet_wrap(~brand, scales = "free_y") +
  scale_y_reordered() +
  
  theme(axis.text.x = element_text(angle = 90)) +
  scale_x_continuous(labels = percent)
```

```{r}
test_set = data.frame(x = c("label", "long label", "very,_very_long_label"), 
                y = c(10, 15, 20))

test_set$newx = str_wrap(test_set$x, width = 10)

ggplot(test_set, aes(newx, y)) + 
  xlab("") + ylab("Number of Participants") +
  geom_bar(stat = "identity")
```


```{r}
youtube %>% 
  na.omit() %>% 
  gather(key = category, value = value, funny:use_sex) %>% 
  
  group_by(category,
           year = 2 * (year %/% 2)) %>% 
  summarise(pct = mean(value), n = n()) %>% 
  
  ggplot(aes(x = year, y = pct, col = category)) +
  geom_path(size = .9) +
  scale_y_continuous(labels = percent) +
  facet_wrap(~category) +
  theme(legend.position = "none")
```


```{r}
youtube %>% 
  na.omit() %>% 
  gather(key = category, value = value, funny:use_sex) %>% 
  
  group_by(category = str_to_title(str_replace_all(category, "_", " ")),
           year = 2 * (year %/% 2)) %>% 
  summarise(pct = mean(value), 
            n = n()) %>% 
  
  ggplot(aes(x = year, y = pct, col = category)) +
  geom_path(size = .9) +
  scale_y_continuous(labels = percent) +
  facet_wrap(~category) +
  theme(legend.position = "none")
```


## Testing Statistically

checking trend statistically related to year or not 

```{r}
glm(animals ~ year, family = "binomial", data = youtube) %>% 
  summary()
```

Statistically above relation doesn't exist

```{r}
glm(celebrity ~ year, family = "binomial", data = youtube) %>% 
  summary()
```

statistically year has relationship with celebrity 

### broom loops

Running loop on all using broom

```{r}
youtube %>% 
  gather(category, value, funny:use_sex) %>% 
  group_by(category) %>% 
  nest()
```


```{r}
youtube %>% 
  gather(category, value, funny:use_sex) %>% 
  group_by(category) %>% 
  summarise(model = list(glm(value ~ year, family = "binomial") )) %>% 
  mutate(td_modeldata = map(model, broom::tidy))
```

```{r}
coefficients <-  youtube %>% 
  # na.omit() %>% 
  gather(category, value, funny:use_sex) %>% 
  group_by(category) %>% 
  summarise(model = list(glm(value ~ year, family = "binomial") )) %>% 
  mutate(td_modeldata = map(model, broom::tidy)) %>% 
  unnest(td_modeldata) %>% 
  filter(term != "(Intercept)") %>% 
  arrange(desc(estimate))

coefficients
```

Categories with Only significant relationship with year 

```{r}
youtube %>% 
  na.omit() %>% 
  gather(key = category, value = value, funny:use_sex) %>% 
  
  group_by(category,
           year = 2 * (year %/% 2)) %>% 
  summarise(pct = mean(value), 
            n = n()) %>% 
  
  inner_join(coefficients, by = "category") %>% 
  mutate(category = str_to_title(str_replace_all(category, "_", " "))) %>% 
  filter(p.value <= .01) %>% 
  
  ggplot(aes(x = year, y = pct, col = category)) +
  geom_path(size = .9) +
  scale_y_continuous(labels = percent) +
  facet_wrap(~category) +
  theme(legend.position = "none")
```




